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Tick Labels

Customize how tick labels appear on your axes.

Mental Model

Tick labels are the text strings displayed at each tick mark — the encoding layer that translates raw numbers into meaning. By default, Matplotlib formats numbers automatically, but you can override them with custom strings (e.g., month names), rotate them for readability, or use FuncFormatter for computed labels. Always set tick positions with set_xticks before setting tick labels with set_xticklabels to keep them aligned.

The same data can be labeled as 1000, 1K, $1,000, Jan, or π/2 — each communicates something different to the reader. Choose labels that match your audience: mathematicians expect π notation, analysts expect currency symbols, general readers expect plain language.

set_xticklabels and set_yticklabels

Set custom text for tick labels:

```python import matplotlib.pyplot as plt import numpy as np

x = np.linspace(-2np.pi, 2np.pi, 100) y = np.sin(x)

fig, ax = plt.subplots(figsize=(12, 3)) ax.plot(x, y)

ax.set_xticks((-2np.pi, -np.pi, 0, np.pi, 2np.pi)) ax.set_yticks((-1, 0, 1)) ax.set_xticklabels(("-2\(\\pi\)", "-\(\\pi\)", "0", "\(\\pi\)", "2\(\\pi\)")) ax.set_yticklabels(("-1", "0", "1"))

ax.spines["top"].set_visible(False) ax.spines["right"].set_visible(False) ax.spines["bottom"].set_position("zero") ax.spines["left"].set_position("zero")

plt.show() ```

Getting Current Labels

python print(ax.get_xticklabels()) print(ax.get_yticklabels())

Returns a list of Text objects.

Rotating Labels

Rotate labels to prevent overlap:

```python import matplotlib.pyplot as plt import numpy as np import pandas as pd

Generate time series data

np.random.seed(0) error = np.random.normal(size=(400,)) index = pd.date_range(start='2019-09-01', end='2020-01-01', freq='D')

mu = 50 data = [mu + 0.4error[t-1] + 0.3error[t-2] + error[t] for t in range(2, len(index)+2)] s = pd.Series(data, index)

fig, ax = plt.subplots(figsize=(12, 3)) ax.plot(s) ax.axhline(mu, linestyle='--', color='grey')

Rotate and align labels

for label in ax.get_xticklabels(): label.set_horizontalalignment("right") label.set_rotation(45)

plt.show() ```

Text Object Properties

Each tick label is a Text object with many properties:

python for label in ax.get_xticklabels(): print(type(label)) # <class 'matplotlib.text.Text'>

Common Text methods:

  • set_rotation(angle): Rotate the text
  • set_horizontalalignment(align): 'left', 'center', 'right'
  • set_verticalalignment(align): 'top', 'center', 'bottom', 'baseline'
  • set_fontsize(size): Set font size
  • set_color(color): Set text color
  • set_bbox(dict): Add background box

Adding Background to Labels

```python import matplotlib.pyplot as plt import numpy as np

x = np.linspace(-10, 10, 500) y = np.sin(x) / x

fig, ax = plt.subplots(figsize=(8, 4)) ax.plot(x, y, linewidth=2)

Move spines to origin

ax.spines['right'].set_color('none') ax.spines['top'].set_color('none') ax.spines['bottom'].set_position(('data', 0)) ax.spines['left'].set_position(('data', 0))

ax.set_xticks([-10, -5, 5, 10]) ax.set_yticks([0.5, 1])

Add white background to labels

for label in ax.get_xticklabels() + ax.get_yticklabels(): label.set_bbox({'facecolor': 'white', 'edgecolor': 'white'})

plt.show() ```

Using tick_params for Styling

Bulk styling of tick labels:

```python ax.tick_params( axis='x', labelsize=12, labelrotation=45, labelcolor='blue' )

ax.tick_params( axis='y', labelsize=10, labelcolor='green' ) ```

Categorical Labels

For bar charts or categorical data:

```python import matplotlib.pyplot as plt

categories = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'] values = [10, 25, 15, 30, 20]

fig, ax = plt.subplots() ax.bar(range(len(categories)), values) ax.set_xticks(range(len(categories))) ax.set_xticklabels(categories) plt.show() ```

Hiding Tick Labels

Keep ticks but hide labels:

python ax.tick_params(labelbottom=False) # Hide x-axis labels ax.tick_params(labelleft=False) # Hide y-axis labels

Or set empty labels:

python ax.set_xticklabels([])

Key Takeaways

  • set_xticklabels() and set_yticklabels() set custom label text
  • Each label is a Text object with full formatting control
  • Use set_rotation() and set_horizontalalignment() for angled labels
  • set_bbox() adds a background to labels
  • tick_params() provides bulk styling options

Exercises

Exercise 1. Plot monthly revenue data and format the y-axis tick labels as currency (e.g., "$10K", "$20K") using FuncFormatter. Use months ['Jan', 'Feb', ..., 'Dec'] as x-tick labels rotated 45 degrees, and values in thousands like [12, 15, 18, 22, 19, 25, 28, 30, 27, 24, 20, 35].

Solution to Exercise 1 
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter

months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
          'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
revenue = [12, 15, 18, 22, 19, 25, 28, 30, 27, 24, 20, 35]

def currency_fmt(x, pos):
    return f'${x:.0f}K'

fig, ax = plt.subplots(figsize=(10, 5))
ax.bar(months, revenue, color='steelblue')
ax.yaxis.set_major_formatter(FuncFormatter(currency_fmt))
ax.tick_params(axis='x', rotation=45)
ax.set_title('Monthly Revenue')
plt.tight_layout()
plt.show()

Exercise 2. Create a plot of y = sin(x) for x in \([0, 2\pi]\) and use FixedLocator to place x-ticks at [0, pi/4, pi/2, 3pi/4, pi, 5pi/4, 3pi/2, 7pi/4, 2pi]. Format the labels as fractions of \(\pi\) (e.g., "\(\pi/4\)", "\(\pi/2\)", "\(3\pi/4\)").

Solution to Exercise 2 
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import FixedLocator

x = np.linspace(0, 2 * np.pi, 500)
y = np.sin(x)

tick_positions = [0, np.pi/4, np.pi/2, 3*np.pi/4, np.pi,
                  5*np.pi/4, 3*np.pi/2, 7*np.pi/4, 2*np.pi]
tick_labels = [r'$0$', r'$\pi/4$', r'$\pi/2$', r'$3\pi/4$', r'$\pi$',
               r'$5\pi/4$', r'$3\pi/2$', r'$7\pi/4$', r'$2\pi$']

fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(x, y)
ax.xaxis.set_major_locator(FixedLocator(tick_positions))
ax.set_xticklabels(tick_labels)
ax.set_title(r'$\sin(x)$ with Fractional $\pi$ Labels')
ax.grid(True, alpha=0.3)
plt.show()

Exercise 3. Generate a time series of 365 daily data points and format the x-axis using mdates.DateFormatter('%b %Y') for month-year labels and mdates.MonthLocator() for monthly ticks. Rotate labels 30 degrees and add minor ticks at weekly intervals.

Solution to Exercise 3 
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np
import pandas as pd

np.random.seed(42)
dates = pd.date_range('2024-01-01', periods=365, freq='D')
values = np.cumsum(np.random.randn(365)) + 100

fig, ax = plt.subplots(figsize=(12, 5))
ax.plot(dates, values, color='steelblue', linewidth=1)

ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))
ax.xaxis.set_minor_locator(mdates.WeekdayLocator())

ax.tick_params(axis='x', rotation=30)
ax.set_title('Daily Time Series')
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

Exercise 4. Create three versions of the same plot (x = [0, π/2, π, 3π/2, 2π], y = sin(x)) with different tick label styles: (a) raw numbers, (b) fraction-of-π notation (0, π/2, π, 3π/2, 2π), (c) degree notation (0°, 90°, 180°, 270°, 360°). Explain when each labeling style is appropriate.

Solution to Exercise 4 
import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 2 * np.pi, 100)
y = np.sin(x)
ticks = [0, np.pi/2, np.pi, 3*np.pi/2, 2*np.pi]

fig, axes = plt.subplots(1, 3, figsize=(15, 4))

# (a) Raw numbers
axes[0].plot(x, y)
axes[0].set_xticks(ticks)
axes[0].set_title('Raw numbers')

# (b) π notation
axes[1].plot(x, y)
axes[1].set_xticks(ticks)
axes[1].set_xticklabels(['0', r'$\pi/2$', r'$\pi$', r'$3\pi/2$', r'$2\pi$'])
axes[1].set_title('π notation (math audience)')

# (c) Degrees
axes[2].plot(x, y)
axes[2].set_xticks(ticks)
axes[2].set_xticklabels(['0°', '90°', '180°', '270°', '360°'])
axes[2].set_title('Degrees (engineering audience)')

plt.tight_layout()
plt.show()

# π notation: for mathematicians and physics papers
# Degrees: for engineers and general audiences
# Raw numbers: when the axis is just a numeric variable